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Require leaf statistics when expanding tree (#4015)

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* Cache left and right gradient sums

* Require leaf statistics when expanding tree
This commit is contained in:
Rory Mitchell 2019-01-18 07:12:20 +02:00 committed by Philip Hyunsu Cho
parent 0f8af85f64
commit 1fc37e4749
11 changed files with 143 additions and 85 deletions
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28
include/xgboost/tree_model.h
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@ -303,14 +303,22 @@ class RegTree {
} }
/** /**
* \brief Expands a leaf node into two additional leaf nodes * \brief Expands a leaf node into two additional leaf nodes.
* *
* \param nid The node index to expand. * \param nid The node index to expand.
* \param split_index Feature index of the split. * \param split_index Feature index of the split.
* \param split_value The split condition. * \param split_value The split condition.
* \param default_left True to default left. * \param default_left True to default left.
* \param base_weight The base weight, before learning rate.
* \param left_leaf_weight The left leaf weight for prediction, modified by learning rate.
* \param right_leaf_weight The right leaf weight for prediction, modified by learning rate.
* \param loss_change The loss change.
* \param sum_hess The sum hess.
*/ */
void ExpandNode(int nid, unsigned split_index, bst_float split_value, bool default_left) { void ExpandNode(int nid, unsigned split_index, bst_float split_value,
bool default_left, bst_float base_weight,
bst_float left_leaf_weight, bst_float right_leaf_weight,
bst_float loss_change, float sum_hess) {
int pleft = this->AllocNode(); int pleft = this->AllocNode();
int pright = this->AllocNode(); int pright = this->AllocNode();
auto &node = nodes_[nid]; auto &node = nodes_[nid];
@ -322,8 +330,12 @@ class RegTree {
node.SetSplit(split_index, split_value, node.SetSplit(split_index, split_value,
default_left); default_left);
// mark right child as 0, to indicate fresh leaf // mark right child as 0, to indicate fresh leaf
nodes_[pleft].SetLeaf(0.0f, 0); nodes_[pleft].SetLeaf(left_leaf_weight, 0);
nodes_[pright].SetLeaf(0.0f, 0); nodes_[pright].SetLeaf(right_leaf_weight, 0);
this->Stat(nid).loss_chg = loss_change;
this->Stat(nid).base_weight = base_weight;
this->Stat(nid).sum_hess = sum_hess;
} }
/*! /*!

13
src/tree/param.h
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@ -354,6 +354,8 @@ struct XGBOOST_ALIGNAS(16) GradStats {
static const int kSimpleStats = 1; static const int kSimpleStats = 1;
/*! \brief constructor, the object must be cleared during construction */ /*! \brief constructor, the object must be cleared during construction */
explicit GradStats(const TrainParam& param) { this->Clear(); } explicit GradStats(const TrainParam& param) { this->Clear(); }
explicit GradStats(double sum_grad, double sum_hess)
: sum_grad(sum_grad), sum_hess(sum_hess) {}
template <typename GpairT> template <typename GpairT>
XGBOOST_DEVICE explicit GradStats(const GpairT &sum) XGBOOST_DEVICE explicit GradStats(const GpairT &sum)
@ -490,8 +492,10 @@ struct SplitEntry {
bst_float loss_chg{0.0f}; bst_float loss_chg{0.0f};
/*! \brief split index */ /*! \brief split index */
unsigned sindex{0}; unsigned sindex{0};
/*! \brief split value */
bst_float split_value{0.0f}; bst_float split_value{0.0f};
GradStats left_sum;
GradStats right_sum;
/*! \brief constructor */ /*! \brief constructor */
SplitEntry() = default; SplitEntry() = default;
/*! /*!
@ -521,6 +525,8 @@ struct SplitEntry {
this->loss_chg = e.loss_chg; this->loss_chg = e.loss_chg;
this->sindex = e.sindex; this->sindex = e.sindex;
this->split_value = e.split_value; this->split_value = e.split_value;
this->left_sum = e.left_sum;
this->right_sum = e.right_sum;
return true; return true;
} else { } else {
return false; return false;
@ -535,7 +541,8 @@ struct SplitEntry {
* \return whether the proposed split is better and can replace current split * \return whether the proposed split is better and can replace current split
*/ */
inline bool Update(bst_float new_loss_chg, unsigned split_index, inline bool Update(bst_float new_loss_chg, unsigned split_index,
bst_float new_split_value, bool default_left) { bst_float new_split_value, bool default_left,
const GradStats &left_sum, const GradStats &right_sum) {
if (this->NeedReplace(new_loss_chg, split_index)) { if (this->NeedReplace(new_loss_chg, split_index)) {
this->loss_chg = new_loss_chg; this->loss_chg = new_loss_chg;
if (default_left) { if (default_left) {
@ -543,6 +550,8 @@ struct SplitEntry {
} }
this->sindex = split_index; this->sindex = split_index;
this->split_value = new_split_value; this->split_value = new_split_value;
this->left_sum = left_sum;
this->right_sum = right_sum;
return true; return true;
} else { } else {
return false; return false;

58
src/tree/updater_colmaker.cc
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@ -311,7 +311,7 @@ class ColMaker: public TreeUpdater {
auto loss_chg = static_cast<bst_float>( auto loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nid, fid, e.stats, c) - spliteval_->ComputeSplitScore(nid, fid, e.stats, c) -
snode_[nid].root_gain); snode_[nid].root_gain);
e.best.Update(loss_chg, fid, fsplit, false); e.best.Update(loss_chg, fid, fsplit, false, e.stats, c);
} }
} }
if (need_backward) { if (need_backward) {
@ -322,7 +322,7 @@ class ColMaker: public TreeUpdater {
auto loss_chg = static_cast<bst_float>( auto loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nid, fid, tmp, c) - spliteval_->ComputeSplitScore(nid, fid, tmp, c) -
snode_[nid].root_gain); snode_[nid].root_gain);
e.best.Update(loss_chg, fid, fsplit, true); e.best.Update(loss_chg, fid, fsplit, true, tmp, c);
} }
} }
} }
@ -335,7 +335,7 @@ class ColMaker: public TreeUpdater {
auto loss_chg = static_cast<bst_float>( auto loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nid, fid, tmp, c) - spliteval_->ComputeSplitScore(nid, fid, tmp, c) -
snode_[nid].root_gain); snode_[nid].root_gain);
e.best.Update(loss_chg, fid, e.last_fvalue + kRtEps, true); e.best.Update(loss_chg, fid, e.last_fvalue + kRtEps, true, tmp, c);
} }
} }
} }
@ -368,7 +368,7 @@ class ColMaker: public TreeUpdater {
spliteval_->ComputeSplitScore(nid, fid, e.stats, c) - spliteval_->ComputeSplitScore(nid, fid, e.stats, c) -
snode_[nid].root_gain); snode_[nid].root_gain);
e.best.Update(loss_chg, fid, (fvalue + e.first_fvalue) * 0.5f, e.best.Update(loss_chg, fid, (fvalue + e.first_fvalue) * 0.5f,
false); false, e.stats, c);
} }
} }
if (need_backward) { if (need_backward) {
@ -379,7 +379,7 @@ class ColMaker: public TreeUpdater {
auto loss_chg = static_cast<bst_float>( auto loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nid, fid, c, cright) - spliteval_->ComputeSplitScore(nid, fid, c, cright) -
snode_[nid].root_gain); snode_[nid].root_gain);
e.best.Update(loss_chg, fid, (fvalue + e.first_fvalue) * 0.5f, true); e.best.Update(loss_chg, fid, (fvalue + e.first_fvalue) * 0.5f, true, c, cright);
} }
} }
} }
@ -410,13 +410,15 @@ class ColMaker: public TreeUpdater {
loss_chg = static_cast<bst_float>( loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nid, fid, c, e.stats) - spliteval_->ComputeSplitScore(nid, fid, c, e.stats) -
snode_[nid].root_gain); snode_[nid].root_gain);
e.best.Update(loss_chg, fid, (fvalue + e.last_fvalue) * 0.5f,
d_step == -1, c, e.stats);
} else { } else {
loss_chg = static_cast<bst_float>( loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nid, fid, e.stats, c) - spliteval_->ComputeSplitScore(nid, fid, e.stats, c) -
snode_[nid].root_gain); snode_[nid].root_gain);
e.best.Update(loss_chg, fid, (fvalue + e.last_fvalue) * 0.5f,
d_step == -1, e.stats, c);
} }
e.best.Update(loss_chg, fid, (fvalue + e.last_fvalue) * 0.5f,
d_step == -1);
} }
} }
// update the statistics // update the statistics
@ -486,18 +488,21 @@ class ColMaker: public TreeUpdater {
if (e.stats.sum_hess >= param_.min_child_weight && if (e.stats.sum_hess >= param_.min_child_weight &&
c.sum_hess >= param_.min_child_weight) { c.sum_hess >= param_.min_child_weight) {
bst_float loss_chg; bst_float loss_chg;
const bst_float gap = std::abs(e.last_fvalue) + kRtEps;
const bst_float delta = d_step == +1 ? gap: -gap;
if (d_step == -1) { if (d_step == -1) {
loss_chg = static_cast<bst_float>( loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nid, fid, c, e.stats) - spliteval_->ComputeSplitScore(nid, fid, c, e.stats) -
snode_[nid].root_gain); snode_[nid].root_gain);
e.best.Update(loss_chg, fid, e.last_fvalue + delta, d_step == -1, c,
e.stats);
} else { } else {
loss_chg = static_cast<bst_float>( loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nid, fid, e.stats, c) - spliteval_->ComputeSplitScore(nid, fid, e.stats, c) -
snode_[nid].root_gain); snode_[nid].root_gain);
e.best.Update(loss_chg, fid, e.last_fvalue + delta, d_step == -1,
e.stats, c);
} }
const bst_float gap = std::abs(e.last_fvalue) + kRtEps;
const bst_float delta = d_step == +1 ? gap: -gap;
e.best.Update(loss_chg, fid, e.last_fvalue + delta, d_step == -1);
} }
} }
} }
@ -545,12 +550,15 @@ class ColMaker: public TreeUpdater {
loss_chg = static_cast<bst_float>( loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nid, fid, c, e.stats) - spliteval_->ComputeSplitScore(nid, fid, c, e.stats) -
snode_[nid].root_gain); snode_[nid].root_gain);
e.best.Update(loss_chg, fid, (fvalue + e.last_fvalue) * 0.5f,
d_step == -1, c, e.stats);
} else { } else {
loss_chg = static_cast<bst_float>( loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nid, fid, e.stats, c) - spliteval_->ComputeSplitScore(nid, fid, e.stats, c) -
snode_[nid].root_gain); snode_[nid].root_gain);
e.best.Update(loss_chg, fid, (fvalue + e.last_fvalue) * 0.5f,
d_step == -1, e.stats, c);
} }
e.best.Update(loss_chg, fid, (fvalue + e.last_fvalue) * 0.5f, d_step == -1);
} }
} }
// update the statistics // update the statistics
@ -565,18 +573,21 @@ class ColMaker: public TreeUpdater {
if (e.stats.sum_hess >= param_.min_child_weight && if (e.stats.sum_hess >= param_.min_child_weight &&
c.sum_hess >= param_.min_child_weight) { c.sum_hess >= param_.min_child_weight) {
bst_float loss_chg; bst_float loss_chg;
GradStats left_sum;
GradStats right_sum;
if (d_step == -1) { if (d_step == -1) {
loss_chg = static_cast<bst_float>( left_sum = c;
spliteval_->ComputeSplitScore(nid, fid, c, e.stats) - right_sum = e.stats;
snode_[nid].root_gain);
} else { } else {
loss_chg = static_cast<bst_float>( left_sum = e.stats;
spliteval_->ComputeSplitScore(nid, fid, e.stats, c) - right_sum = c;
snode_[nid].root_gain);
} }
loss_chg = static_cast<bst_float>(
spliteval_->ComputeSplitScore(nid, fid, left_sum, right_sum) -
snode_[nid].root_gain);
const bst_float gap = std::abs(e.last_fvalue) + kRtEps; const bst_float gap = std::abs(e.last_fvalue) + kRtEps;
const bst_float delta = d_step == +1 ? gap: -gap; const bst_float delta = d_step == +1 ? gap: -gap;
e.best.Update(loss_chg, fid, e.last_fvalue + delta, d_step == -1); e.best.Update(loss_chg, fid, e.last_fvalue + delta, d_step == -1, left_sum, right_sum);
} }
} }
} }
@ -637,7 +648,16 @@ class ColMaker: public TreeUpdater {
NodeEntry &e = snode_[nid]; NodeEntry &e = snode_[nid];
// now we know the solution in snode[nid], set split // now we know the solution in snode[nid], set split
if (e.best.loss_chg > kRtEps) { if (e.best.loss_chg > kRtEps) {
p_tree->ExpandNode(nid, e.best.SplitIndex(), e.best.split_value, e.best.DefaultLeft()); bst_float left_leaf_weight =
spliteval_->ComputeWeight(nid, e.best.left_sum) *
param_.learning_rate;
bst_float right_leaf_weight =
spliteval_->ComputeWeight(nid, e.best.right_sum) *
param_.learning_rate;
p_tree->ExpandNode(nid, e.best.SplitIndex(), e.best.split_value,
e.best.DefaultLeft(), e.weight, left_leaf_weight,
right_leaf_weight, e.best.loss_chg,
e.stats.sum_hess);
} else { } else {
(*p_tree)[nid].SetLeaf(e.weight * param_.learning_rate); (*p_tree)[nid].SetLeaf(e.weight * param_.learning_rate);
} }

3
src/tree/updater_gpu_common.cuh
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@ -296,7 +296,8 @@ inline void Dense2SparseTree(RegTree* p_tree,
for (int gpu_nid = 0; gpu_nid < h_nodes.size(); gpu_nid++) { for (int gpu_nid = 0; gpu_nid < h_nodes.size(); gpu_nid++) {
const DeviceNodeStats& n = h_nodes[gpu_nid]; const DeviceNodeStats& n = h_nodes[gpu_nid];
if (!n.IsUnused() && !n.IsLeaf()) { if (!n.IsUnused() && !n.IsLeaf()) {
tree.ExpandNode(nid, n.fidx, n.fvalue, n.dir == kLeftDir); tree.ExpandNode(nid, n.fidx, n.fvalue, n.dir == kLeftDir, n.weight, 0.0f,
0.0f, n.root_gain, n.sum_gradients.GetHess());
tree.Stat(nid).loss_chg = n.root_gain; tree.Stat(nid).loss_chg = n.root_gain;
tree.Stat(nid).base_weight = n.weight; tree.Stat(nid).base_weight = n.weight;
tree.Stat(nid).sum_hess = n.sum_gradients.GetHess(); tree.Stat(nid).sum_hess = n.sum_gradients.GetHess();

45
src/tree/updater_gpu_hist.cu
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@ -1182,42 +1182,35 @@ class GPUHistMakerSpecialised{
} }
void ApplySplit(const ExpandEntry& candidate, RegTree* p_tree) { void ApplySplit(const ExpandEntry& candidate, RegTree* p_tree) {
// Add new leaves
RegTree& tree = *p_tree; RegTree& tree = *p_tree;
tree.ExpandNode(candidate.nid, candidate.split.findex, candidate.split.fvalue,
candidate.split.dir == kLeftDir);
auto& parent = tree[candidate.nid];
tree.Stat(candidate.nid).loss_chg = candidate.split.loss_chg;
// Set up child constraints
node_value_constraints_.resize(tree.GetNodes().size());
GradStats left_stats(param_); GradStats left_stats(param_);
left_stats.Add(candidate.split.left_sum); left_stats.Add(candidate.split.left_sum);
GradStats right_stats(param_); GradStats right_stats(param_);
right_stats.Add(candidate.split.right_sum); right_stats.Add(candidate.split.right_sum);
node_value_constraints_[candidate.nid].SetChild( GradStats parent_sum(param_);
param_, parent.SplitIndex(), left_stats, right_stats, parent_sum.Add(left_stats);
&node_value_constraints_[parent.LeftChild()], parent_sum.Add(right_stats);
&node_value_constraints_[parent.RightChild()]); node_value_constraints_.resize(tree.GetNodes().size());
auto base_weight = node_value_constraints_[candidate.nid].CalcWeight(param_, parent_sum);
// Configure left child
auto left_weight = auto left_weight =
node_value_constraints_[parent.LeftChild()].CalcWeight(param_, left_stats); node_value_constraints_[candidate.nid].CalcWeight(param_, left_stats)*param_.learning_rate;
tree[parent.LeftChild()].SetLeaf(left_weight * param_.learning_rate, 0);
tree.Stat(parent.LeftChild()).base_weight = left_weight;
tree.Stat(parent.LeftChild()).sum_hess = candidate.split.left_sum.GetHess();
// Configure right child
auto right_weight = auto right_weight =
node_value_constraints_[parent.RightChild()].CalcWeight(param_, right_stats); node_value_constraints_[candidate.nid].CalcWeight(param_, right_stats)*param_.learning_rate;
tree[parent.RightChild()].SetLeaf(right_weight * param_.learning_rate, 0); tree.ExpandNode(candidate.nid, candidate.split.findex,
tree.Stat(parent.RightChild()).base_weight = right_weight; candidate.split.fvalue, candidate.split.dir == kLeftDir,
tree.Stat(parent.RightChild()).sum_hess = candidate.split.right_sum.GetHess(); base_weight, left_weight, right_weight,
candidate.split.loss_chg, parent_sum.sum_hess);
// Set up child constraints
node_value_constraints_.resize(tree.GetNodes().size());
node_value_constraints_[candidate.nid].SetChild(
param_, tree[candidate.nid].SplitIndex(), left_stats, right_stats,
&node_value_constraints_[tree[candidate.nid].LeftChild()],
&node_value_constraints_[tree[candidate.nid].RightChild()]);
// Store sum gradients // Store sum gradients
for (auto& shard : shards_) { for (auto& shard : shards_) {
shard->node_sum_gradients[parent.LeftChild()] = candidate.split.left_sum; shard->node_sum_gradients[tree[candidate.nid].LeftChild()] = candidate.split.left_sum;
shard->node_sum_gradients[parent.RightChild()] = candidate.split.right_sum; shard->node_sum_gradients[tree[candidate.nid].RightChild()] = candidate.split.right_sum;
} }
} }

17
src/tree/updater_histmaker.cc
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@ -192,7 +192,8 @@ class HistMaker: public BaseMaker {
c.SetSubstract(node_sum, s); c.SetSubstract(node_sum, s);
if (c.sum_hess >= param_.min_child_weight) { if (c.sum_hess >= param_.min_child_weight) {
double loss_chg = s.CalcGain(param_) + c.CalcGain(param_) - root_gain; double loss_chg = s.CalcGain(param_) + c.CalcGain(param_) - root_gain;
if (best->Update(static_cast<bst_float>(loss_chg), fid, hist.cut[i], false)) { if (best->Update(static_cast<bst_float>(loss_chg), fid, hist.cut[i],
false, s, c)) {
*left_sum = s; *left_sum = s;
} }
} }
@ -205,7 +206,7 @@ class HistMaker: public BaseMaker {
c.SetSubstract(node_sum, s); c.SetSubstract(node_sum, s);
if (c.sum_hess >= param_.min_child_weight) { if (c.sum_hess >= param_.min_child_weight) {
double loss_chg = s.CalcGain(param_) + c.CalcGain(param_) - root_gain; double loss_chg = s.CalcGain(param_) + c.CalcGain(param_) - root_gain;
if (best->Update(static_cast<bst_float>(loss_chg), fid, hist.cut[i-1], true)) { if (best->Update(static_cast<bst_float>(loss_chg), fid, hist.cut[i-1], true, c, s)) {
*left_sum = c; *left_sum = c;
} }
} }
@ -243,8 +244,18 @@ class HistMaker: public BaseMaker {
p_tree->Stat(nid).loss_chg = best.loss_chg; p_tree->Stat(nid).loss_chg = best.loss_chg;
// now we know the solution in snode[nid], set split // now we know the solution in snode[nid], set split
if (best.loss_chg > kRtEps) { if (best.loss_chg > kRtEps) {
bst_float base_weight = node_sum.CalcWeight(param_);
bst_float left_leaf_weight =
CalcWeight(param_, best.left_sum.sum_grad, best.left_sum.sum_hess) *
param_.learning_rate;
bst_float right_leaf_weight =
CalcWeight(param_, best.right_sum.sum_grad,
best.right_sum.sum_hess) *
param_.learning_rate;
p_tree->ExpandNode(nid, best.SplitIndex(), best.split_value, p_tree->ExpandNode(nid, best.SplitIndex(), best.split_value,
best.DefaultLeft()); best.DefaultLeft(), base_weight, left_leaf_weight,
right_leaf_weight, best.loss_chg,
node_sum.sum_hess);
// right side sum // right side sum
TStats right_sum; TStats right_sum;
right_sum.SetSubstract(node_sum, left_sum[wid]); right_sum.SetSubstract(node_sum, left_sum[wid]);

10
src/tree/updater_quantile_hist.cc
View File

@ -429,8 +429,13 @@ void QuantileHistMaker::Builder::ApplySplit(int nid,
/* 1. Create child nodes */ /* 1. Create child nodes */
NodeEntry& e = snode_[nid]; NodeEntry& e = snode_[nid];
bst_float left_leaf_weight =
spliteval_->ComputeWeight(nid, e.best.left_sum) * param_.learning_rate;
bst_float right_leaf_weight =
spliteval_->ComputeWeight(nid, e.best.right_sum) * param_.learning_rate;
p_tree->ExpandNode(nid, e.best.SplitIndex(), e.best.split_value, p_tree->ExpandNode(nid, e.best.SplitIndex(), e.best.split_value,
e.best.DefaultLeft()); e.best.DefaultLeft(), e.weight, left_leaf_weight,
right_leaf_weight, e.best.loss_chg, e.stats.sum_hess);
/* 2. Categorize member rows */ /* 2. Categorize member rows */
const auto nthread = static_cast<bst_omp_uint>(this->nthread_); const auto nthread = static_cast<bst_omp_uint>(this->nthread_);
@ -698,6 +703,7 @@ void QuantileHistMaker::Builder::EnumerateSplit(int d_step,
spliteval_->ComputeSplitScore(nodeID, fid, e, c) - spliteval_->ComputeSplitScore(nodeID, fid, e, c) -
snode.root_gain); snode.root_gain);
split_pt = cut_val[i]; split_pt = cut_val[i];
best.Update(loss_chg, fid, split_pt, d_step == -1, e, c);
} else { } else {
// backward enumeration: split at left bound of each bin // backward enumeration: split at left bound of each bin
loss_chg = static_cast<bst_float>( loss_chg = static_cast<bst_float>(
@ -709,8 +715,8 @@ void QuantileHistMaker::Builder::EnumerateSplit(int d_step,
} else { } else {
split_pt = cut_val[i - 1]; split_pt = cut_val[i - 1];
} }
best.Update(loss_chg, fid, split_pt, d_step == -1, c, e);
} }
best.Update(loss_chg, fid, split_pt, d_step == -1);
} }
} }
} }

27
src/tree/updater_skmaker.cc
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@ -281,12 +281,21 @@ class SketchMaker: public BaseMaker {
const int nid = qexpand_[wid]; const int nid = qexpand_[wid];
const SplitEntry &best = sol[wid]; const SplitEntry &best = sol[wid];
// set up the values // set up the values
p_tree->Stat(nid).loss_chg = best.loss_chg;
this->SetStats(nid, node_stats_[nid], p_tree); this->SetStats(nid, node_stats_[nid], p_tree);
// now we know the solution in snode[nid], set split // now we know the solution in snode[nid], set split
if (best.loss_chg > kRtEps) { if (best.loss_chg > kRtEps) {
bst_float base_weight = node_stats_[nid].CalcWeight(param_);
bst_float left_leaf_weight =
CalcWeight(param_, best.left_sum.sum_grad, best.left_sum.sum_hess) *
param_.learning_rate;
bst_float right_leaf_weight =
CalcWeight(param_, best.right_sum.sum_grad,
best.right_sum.sum_hess) *
param_.learning_rate;
p_tree->ExpandNode(nid, best.SplitIndex(), best.split_value, p_tree->ExpandNode(nid, best.SplitIndex(), best.split_value,
best.DefaultLeft()); best.DefaultLeft(), base_weight, left_leaf_weight,
right_leaf_weight, best.loss_chg,
node_stats_[nid].sum_hess);
} else { } else {
(*p_tree)[nid].SetLeaf(p_tree->Stat(nid).base_weight * param_.learning_rate); (*p_tree)[nid].SetLeaf(p_tree->Stat(nid).base_weight * param_.learning_rate);
} }
@ -336,7 +345,9 @@ class SketchMaker: public BaseMaker {
if (s.sum_hess >= param_.min_child_weight && if (s.sum_hess >= param_.min_child_weight &&
c.sum_hess >= param_.min_child_weight) { c.sum_hess >= param_.min_child_weight) {
double loss_chg = s.CalcGain(param_) + c.CalcGain(param_) - root_gain; double loss_chg = s.CalcGain(param_) + c.CalcGain(param_) - root_gain;
best->Update(static_cast<bst_float>(loss_chg), fid, fsplits[i], false); best->Update(static_cast<bst_float>(loss_chg), fid, fsplits[i], false,
GradStats(s.pos_grad - s.neg_grad , s.sum_hess),
GradStats(c.pos_grad - c.neg_grad, c.sum_hess));
} }
// backward // backward
c.SetSubstract(feat_sum, s); c.SetSubstract(feat_sum, s);
@ -344,7 +355,9 @@ class SketchMaker: public BaseMaker {
if (s.sum_hess >= param_.min_child_weight && if (s.sum_hess >= param_.min_child_weight &&
c.sum_hess >= param_.min_child_weight) { c.sum_hess >= param_.min_child_weight) {
double loss_chg = s.CalcGain(param_) + c.CalcGain(param_) - root_gain; double loss_chg = s.CalcGain(param_) + c.CalcGain(param_) - root_gain;
best->Update(static_cast<bst_float>(loss_chg), fid, fsplits[i], true); best->Update(static_cast<bst_float>(loss_chg), fid, fsplits[i], true,
GradStats(s.pos_grad - s.neg_grad, s.sum_hess),
GradStats(c.pos_grad - c.neg_grad, c.sum_hess));
} }
} }
{ {
@ -355,8 +368,10 @@ class SketchMaker: public BaseMaker {
c.sum_hess >= param_.min_child_weight) { c.sum_hess >= param_.min_child_weight) {
bst_float cpt = fsplits.back(); bst_float cpt = fsplits.back();
double loss_chg = s.CalcGain(param_) + c.CalcGain(param_) - root_gain; double loss_chg = s.CalcGain(param_) + c.CalcGain(param_) - root_gain;
best->Update(static_cast<bst_float>(loss_chg), best->Update(static_cast<bst_float>(loss_chg), fid,
fid, cpt + std::abs(cpt) + 1.0f, false); cpt + std::abs(cpt) + 1.0f, false,
GradStats(s.pos_grad - s.neg_grad, s.sum_hess),
GradStats(c.pos_grad - c.neg_grad, c.sum_hess));
} }
} }
} }

9
tests/cpp/tree/test_param.cc
View File

@ -82,12 +82,15 @@ TEST(Param, SplitEntry) {
xgboost::tree::SplitEntry se2; xgboost::tree::SplitEntry se2;
EXPECT_FALSE(se1.Update(se2)); EXPECT_FALSE(se1.Update(se2));
EXPECT_FALSE(se2.Update(-1, 100, 0, true)); EXPECT_FALSE(se2.Update(-1, 100, 0, true, xgboost::tree::GradStats(),
ASSERT_TRUE(se2.Update(1, 100, 0, true)); xgboost::tree::GradStats()));
ASSERT_TRUE(se2.Update(1, 100, 0, true, xgboost::tree::GradStats(),
xgboost::tree::GradStats()));
ASSERT_TRUE(se1.Update(se2)); ASSERT_TRUE(se1.Update(se2));
xgboost::tree::SplitEntry se3; xgboost::tree::SplitEntry se3;
se3.Update(2, 101, 0, false); se3.Update(2, 101, 0, false, xgboost::tree::GradStats(),
xgboost::tree::GradStats());
xgboost::tree::SplitEntry::Reduce(se2, se3); xgboost::tree::SplitEntry::Reduce(se2, se3);
EXPECT_EQ(se2.SplitIndex(), 101); EXPECT_EQ(se2.SplitIndex(), 101);
EXPECT_FALSE(se2.DefaultLeft()); EXPECT_FALSE(se2.DefaultLeft());

13
tests/cpp/tree/test_prune.cc
View File

@ -38,22 +38,13 @@ TEST(Updater, Prune) {
pruner->Init(cfg); pruner->Init(cfg);
// loss_chg < min_split_loss; // loss_chg < min_split_loss;
tree.ExpandNode(0, 0, 0, true); tree.ExpandNode(0, 0, 0, true, 0.0f, 0.3f, 0.4f, 0.0f, 0.0f);
int cleft = tree[0].LeftChild();
int cright = tree[0].RightChild();
tree[cleft].SetLeaf(0.3f, 0);
tree[cright].SetLeaf(0.4f, 0);
pruner->Update(&gpair, dmat->get(), trees); pruner->Update(&gpair, dmat->get(), trees);
ASSERT_EQ(tree.NumExtraNodes(), 0); ASSERT_EQ(tree.NumExtraNodes(), 0);
// loss_chg > min_split_loss; // loss_chg > min_split_loss;
tree.ExpandNode(0, 0, 0, true); tree.ExpandNode(0, 0, 0, true, 0.0f, 0.3f, 0.4f, 11.0f, 0.0f);
cleft = tree[0].LeftChild();
cright = tree[0].RightChild();
tree[cleft].SetLeaf(0.3f, 0);
tree[cright].SetLeaf(0.4f, 0);
tree.Stat(0).loss_chg = 11;
pruner->Update(&gpair, dmat->get(), trees); pruner->Update(&gpair, dmat->get(), trees);
ASSERT_EQ(tree.NumExtraNodes(), 2); ASSERT_EQ(tree.NumExtraNodes(), 2);

5
tests/cpp/tree/test_refresh.cc
View File

@ -29,12 +29,9 @@ TEST(Updater, Refresh) {
std::vector<RegTree*> trees {&tree}; std::vector<RegTree*> trees {&tree};
std::unique_ptr<TreeUpdater> refresher(TreeUpdater::Create("refresh")); std::unique_ptr<TreeUpdater> refresher(TreeUpdater::Create("refresh"));
tree.ExpandNode(0, 0, 0, true); tree.ExpandNode(0, 2, 0.2f, false, 0.0, 0.2f, 0.8f, 0.0f, 0.0f);
int cleft = tree[0].LeftChild(); int cleft = tree[0].LeftChild();
int cright = tree[0].RightChild(); int cright = tree[0].RightChild();
tree[cleft].SetLeaf(0.2f, 0);
tree[cright].SetLeaf(0.8f, 0);
tree[0].SetSplit(2, 0.2f);
tree.Stat(cleft).base_weight = 1.2; tree.Stat(cleft).base_weight = 1.2;
tree.Stat(cright).base_weight = 1.3; tree.Stat(cright).base_weight = 1.3;